This study centers on the 3D simulation localization technology of power cable fault points, using the combination of wavelet transform and neural network, aiming to improve the accuracy and efficiency of cable fault localization. This paper adopts the method of combining wavelet transform and genetic algorithm optimization of back propagation (GA-BP) neural network. First, by constructing a three-dimensional simulation model of power cables, the wavelet transform is applied to extract fault features and the GA-BP neural network is utilized for fault point localization. The experimental results show that the average localization errors of this method in single-phase ground fault and two-phase grounded short-circuit fault are 0.112km and 0.126km, respectively, which are significantly better than the traditional method. Under different fault initial phase angle conditions, the proposed method shows strong adaptability and the error is controlled within 1%. Meanwhile, the present algorithm exhibits strong noise suppression ability under white and colored noise backgrounds, especially in low signal-to-noise ratio environments. In summary, this study demonstrates the effectiveness of the 3D simulation localization technique for power cable fault points combining wavelet transform and GA-BP neural network in improving the localization accuracy and noise resistance.
In this paper, finite element analysis of power cable connection line faults based on the time-domain reflection (TDR) method is designed to improve the accuracy and efficiency of fault detection. In terms of methodology, a distributed parameter model is used to describe the cable, and based on the fluctuation equation and characteristic impedance theory, the impedance characteristics under different fault types are analyzed. In terms of results, the effectiveness of the method is verified by mathematical modeling and simulation of single-phase ground faults and disconnection faults through the Simulink platform in MATLAB. The simulation results show that in terms of fault localization accuracy, our method controls the error within 0.07m, which is significantly improved compared with the traditional method. In addition, the fault detection under different noise signals is analyzed, which further establishes the accuracy of the TDR method in judging different fault types. The TDR-based power cable fault detection method proposed in this study not only improves the accuracy of fault localization, but also provides a reliable theoretical basis for the identification of different types of faults, which contributes to the stable operation and maintenance of power systems
Based on computer information technology, this paper constructs a digital library information integration system. Firstly, the system is divided into information retrieval module and personalized service module according to the user's demand, and then the WebServices service is used to realize data integration and the library information obtained. And according to the principle of Fourier transform can be known to realize the conversion of information and knowledge. Finally, according to the movement state of knowledge expression, the formation of the wizard information base thus realizing information rectification. The structure shows that the digital library information integration system can provide 24h*14 hours of service, and the average response time of each operation is within 1s. It shows that the application of computer information technology in digital libraries can provide users with personalized services and perfect their interests.
In modern linguistic research, the application of Artificial Intelligence has led the field and provided powerful tools and prospects for linguists. LSTM is used for extracting character features, joint vector representation and constructing text generation models and generating natural language text. LSTM is involved in the design of speech recognition network to process the input speech signals for generators and discriminators to improve the accuracy of speech recognition. By continuously optimizing the training objectives, the translation system will more accurately translate text from one language to another, thus facilitating cross-cultural communication. Through the application of artificial intelligence, the F1 value has been improved by 3.9% compared with the previous value, and the cumulative variance contribution rate of the five factors is more than 60%, with all subloadings reaching 0.4 or more. Artificial intelligence will promote the development of the field of linguistics, improve research efficiency and accuracy, and promote the innovation of language technology.
With the complexity of power systems and the increase of loads, power cable faults occur frequently, affecting the safety of power supply. A parameter optimization-based variational modal decomposition (VMD) technique combined with wavelet transform is used. Through in-depth analysis of cable fault signals, this study first applies VMD to decompose the signals, and then further analyzes the processed signals in combination with wavelet transform. The experimental results show that the method achieves a significant reduction in the average localization error in the simulated fault test, with an average error of less than 1%, which improves the localization accuracy by about 30% compared with the traditional method. In the processing of complex fault signals, the present method shows better adaptability and robustness. Overall, this study not only improves the accuracy of fault localization, but also provides a new technical path for the diagnosis and maintenance of power system faults.
